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A tool for extraction and collaboration for data curation related to biology. CBioC runs as a web browser extension and allows unobtrusive use of the system during the regular course of research in PubMed. It can also be accessed directly (without having to install a plug-in). Automated text extraction is used as a starting point to bootstrap the database, but then it is up to biologists improve upon the extracted data, ironing out inconsistencies by subsequent edits on a massive scale. * After install, it loads when you visit PubMed. * Gets interactions from PubMed abstracts. * Allows you to vote and modify extracted data. * Also shows data from BIND, DIP, MINT, GRID, IntAct.

URL: http://www.cbioc.org

Resource ID: nlx_149235     Resource Type: Resource     Version: Latest Version


text extraction, curation, crowd sourcing

Funding Information

Arizona State University; Arizona; USA, 0412000

Additional Resource Types

Software Resource



Related To




Parent Organization


CBioC - Collaboratively uncovering the nuggets of knowledge buried in millions of biomedical texts, Collaborative Bio Curation


Website last updated 2005; Latest news from 2007

Listed By


Publication Link


Original Submitter


Version Status


Submitted On

12:00am July 12, 2012

Originated From


Changes from Previous Version

    No Changes

    Version 4

    Created 1 week ago by Christie Wang

    Version 3

    Created 2 weeks ago by Christie Wang

    Version 2

    Created 2 weeks ago by Christie Wang

    Version 1

    Created 3 years ago by Anonymous

    CBioC: beyond a prototype for collaborative annotation of molecular interactions from the literature.

    • Baral C
    • Comput Syst Bioinformatics Conf
    • 2007 22

    In molecular biology research, looking for information on a particular entity such as a gene or a protein may lead to thousands of articles, making it impossible for a researcher to individually read these articles and even just their abstracts. Thus, there is a need to curate the literature to get various nuggets of knowledge, such as an interaction between two proteins, and store them in a database. However the body of existing biomedical articles is growing at a very fast rate, making it impossible to curate them manually. An alternative approach of using computers for automatic extraction has problem with accuracy. We propose to leverage the advantages of both techniques, extracting binary relationships between biological entities automatically from the biomedical literature and providing a platform that allows community collaboration in the annotation of the extracted relationships. Thus, the community of researchers that writes and reads the biomedical texts can use the server for searching our database of extracted facts, and as an easy-to-use web platform to annotate facts relevant to them. We presented a preliminary prototype as a proof of concept earlier(1). This paper presents the working implementation available for download at http://www.cbioc.org as a browser-plug in for both Internet Explorer and FireFox. This current version has been available since June of 2006, and has over 160 registered users from around the world. Aside from its use as an annotation tool, data from CBioC has also been used in computational methods with encouraging results.